"""simple docstring"""


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


a				=logging.getLogger(__name__)





def      lowerCamelCase_					(   __lowerCAmelCase=2  ,       __lowerCAmelCase=3  ,       __lowerCAmelCase=16  ,       __lowerCAmelCase = 10  ,       __lowerCAmelCase = 2							)				->   Optional[Any]:




						'''simple docstring'''



						def get_dataset(__lowerCAmelCase							):
												lowerCamelCase__   =torch.randn(batch_size * n_batches  ,       1							)
												return TensorDataset(__lowerCAmelCase  ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches  ,       1							)							)

						lowerCamelCase__   =get_dataset(__lowerCAmelCase							)
						lowerCamelCase__   =get_dataset(__lowerCAmelCase							)
						lowerCamelCase__   =DataLoader(__lowerCAmelCase  ,       shuffle=__lowerCAmelCase  ,       batch_size=__lowerCAmelCase  ,       num_workers=4							)
						lowerCamelCase__   =DataLoader(__lowerCAmelCase  ,       shuffle=__lowerCAmelCase  ,       batch_size=__lowerCAmelCase  ,       num_workers=4							)
						return (train_dataloader, valid_dataloader)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=None							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =[]
						for epoch in range(__lowerCAmelCase							):
												# Train quickly
												model.train()
												for batch in dataloader:
																		lowerCamelCase__   , lowerCamelCase__   =batch
																		lowerCamelCase__   =model(__lowerCAmelCase							)
																		lowerCamelCase__   =torch.nn.functional.mse_loss(__lowerCAmelCase  ,       __lowerCAmelCase							)
																		accelerator.backward(__lowerCAmelCase							)
																		optimizer.step()
																		optimizer.zero_grad()
												rands.append(random.random()							)  # Introduce some randomness
												if scheduler is not None:
																		scheduler.step()
						return rands






class   __UpperCAmelCase		(      nn.Module      ):

				def __init__( self ):
										super().__init__()
										lowerCamelCase__   =nn.Parameter(torch.randn(1 ) )
										lowerCamelCase__   =nn.Parameter(torch.randn(1 ) )






				def 						_a      ( self      ,					_lowerCamelCase ):
										return x * self.a + self.b






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =torch.optim.Adam(params=model.parameters()      ,					lr=1E-3 )
																lowerCamelCase__   , lowerCamelCase__   =dummy_dataloaders()
																lowerCamelCase__   =ProjectConfiguration(total_limit=1      ,					project_dir=_lowerCamelCase      ,					automatic_checkpoint_naming=_lowerCamelCase )
																# Train baseline
																lowerCamelCase__   =Accelerator(project_config=_lowerCamelCase )
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																# Save initial
																accelerator.save_state()

																# Save second state
																accelerator.save_state()
																self.assertEqual(len(os.listdir(accelerator.project_dir ) )      ,					1 )

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =torch.optim.Adam(params=model.parameters()      ,					lr=1E-3 )
																lowerCamelCase__   , lowerCamelCase__   =dummy_dataloaders()
																# Train baseline
																lowerCamelCase__   =Accelerator()
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																# Save initial
																lowerCamelCase__   =os.path.join(_lowerCamelCase      ,					"initial" )
																accelerator.save_state(_lowerCamelCase )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()
																lowerCamelCase__   =train(3      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()

																# Train partially
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =torch.optim.Adam(params=model.parameters()      ,					lr=1E-3 )
																lowerCamelCase__   , lowerCamelCase__   =dummy_dataloaders()
																lowerCamelCase__   =Accelerator()
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																accelerator.load_state(_lowerCamelCase )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =train(2      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																# Save everything
																lowerCamelCase__   =os.path.join(_lowerCamelCase      ,					"checkpoint" )
																accelerator.save_state(_lowerCamelCase )

																# Load everything back in and make sure all states work
																accelerator.load_state(_lowerCamelCase )
																test_rands += train(1      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =torch.optim.Adam(params=model.parameters()      ,					lr=1E-3 )
																lowerCamelCase__   , lowerCamelCase__   =dummy_dataloaders()
																lowerCamelCase__   =ProjectConfiguration(automatic_checkpoint_naming=_lowerCamelCase )

																# Train baseline
																lowerCamelCase__   =Accelerator(project_dir=_lowerCamelCase      ,					project_config=_lowerCamelCase )
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																# Save initial
																accelerator.save_state()
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()
																lowerCamelCase__   =train(3      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()

																# Train partially
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =torch.optim.Adam(params=model.parameters()      ,					lr=1E-3 )
																lowerCamelCase__   , lowerCamelCase__   =dummy_dataloaders()
																lowerCamelCase__   =ProjectConfiguration(iteration=1      ,					automatic_checkpoint_naming=_lowerCamelCase )
																lowerCamelCase__   =Accelerator(project_dir=_lowerCamelCase      ,					project_config=_lowerCamelCase )
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																accelerator.load_state(os.path.join(_lowerCamelCase      ,					"checkpoints"      ,					"checkpoint_0" ) )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )

																lowerCamelCase__   =train(2      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																# Save everything
																accelerator.save_state()

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(_lowerCamelCase      ,					"checkpoints"      ,					"checkpoint_1" ) )
																test_rands += train(1      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																((lowerCamelCase__)   , (lowerCamelCase__))   =model.a.item(), model.b.item()
																lowerCamelCase__   =optimizer.state_dict()
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )
																self.assertEqual(_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =torch.tensor([1, 2, 3] )
										lowerCamelCase__   =torch.tensor([2, 3, 4] )
										lowerCamelCase__   =DummyModel()
										lowerCamelCase__   =torch.optim.Adam(net.parameters() )
										lowerCamelCase__   =Accelerator()
										with self.assertRaises(_lowerCamelCase ) as ve:
																accelerator.register_for_checkpointing(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =str(ve.exception )
										self.assertTrue("Item at index 0" in message )
										self.assertTrue("Item at index 1" in message )
										self.assertFalse("Item at index 2" in message )
										self.assertFalse("Item at index 3" in message )

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =torch.optim.Adam(params=model.parameters()      ,					lr=1E-3 )
																lowerCamelCase__   =torch.optim.lr_scheduler.StepLR(_lowerCamelCase      ,					step_size=1      ,					gamma=0.9_9 )
																lowerCamelCase__   , lowerCamelCase__   =dummy_dataloaders()
																lowerCamelCase__   =ProjectConfiguration(automatic_checkpoint_naming=_lowerCamelCase )
																# Train baseline
																lowerCamelCase__   =Accelerator(project_dir=_lowerCamelCase      ,					project_config=_lowerCamelCase )
																lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																# Save initial
																accelerator.save_state()
																lowerCamelCase__   =scheduler.state_dict()
																train(3      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase )
																self.assertNotEqual(_lowerCamelCase      ,					scheduler.state_dict() )

																# Load everything back in and make sure all states work
																accelerator.load_state(os.path.join(_lowerCamelCase      ,					"checkpoints"      ,					"checkpoint_0" ) )
																self.assertEqual(_lowerCamelCase      ,					scheduler.state_dict() )

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmpdir:
																set_seed(42 )
																lowerCamelCase__   =DummyModel()
																lowerCamelCase__   =ProjectConfiguration(automatic_checkpoint_naming=_lowerCamelCase      ,					total_limit=2 )
																# Train baseline
																lowerCamelCase__   =Accelerator(project_dir=_lowerCamelCase      ,					project_config=_lowerCamelCase )
																lowerCamelCase__   =accelerator.prepare(_lowerCamelCase )
																# Save 3 states:
																for _ in range(11 ):
																						accelerator.save_state()
																self.assertTrue(not os.path.exists(os.path.join(_lowerCamelCase      ,					"checkpoints"      ,					"checkpoint_0" ) ) )
																self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase      ,					"checkpoints"      ,					"checkpoint_9" ) ) )
																self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase      ,					"checkpoints"      ,					"checkpoint_10" ) ) )






				@require_cuda
				def 						_a      ( self ):
										lowerCamelCase__   =["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
										execute_subprocess_async(_lowerCamelCase      ,					env=os.environ.copy() )


if __name__ == "__main__":
	a				='/tmp/accelerate/state_checkpointing'
	a				=DummyModel()
	a				=torch.optim.Adam(params=model.parameters(), lr=1E-3)
	a				=torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
	a				, a				=dummy_dataloaders()
	a				=ProjectConfiguration(automatic_checkpoint_naming=True)
	# Train baseline
	a				=Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
	if accelerator.process_index == 0:
		if os.path.exists(savedir):
			shutil.rmtree(savedir)
		os.makedirs(savedir)
	a				, a				, a				, a				, a				=accelerator.prepare(
	    model, optimizer, train_dataloader, valid_dataloader, scheduler
	)
	a				, a				=accelerator.prepare(model, optimizer)
	train(3, model, train_dataloader, optimizer, accelerator, scheduler)
	# Check that the intial optimizer is loaded on the GPU
	for group in optimizer.param_groups:
		a				=group['params'][0].device
		break
	assert param_device.type == accelerator.device.type
	a				=model.cpu()
	accelerator.wait_for_everyone()
	accelerator.save_state()
	accelerator.wait_for_everyone()

	# Check CPU state
	accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
	for group in optimizer.param_groups:
		a				=group['params'][0].device
		break
	assert (
	    param_device.type == torch.device('cpu').type
	), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

	# Check device state
	model.to(accelerator.device)
	accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
	for group in optimizer.param_groups:
		a				=group['params'][0].device
		break
	assert (
	    param_device.type == accelerator.device.type
	), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

	# Check error
	with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
		accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
	accelerator.wait_for_everyone()
	if accelerator.process_index == 0:
		shutil.rmtree(savedir)
	accelerator.wait_for_everyone()



"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


